Prediction from a fitted textmodel_nb object

predict.textmodel_nb() implements class predictions from a fitted Naive Bayes model. using trained Naive Bayes examples

# S3 method for textmodel_nb
predict(
  object,
  newdata = NULL,
  type = c("class", "probability", "logposterior"),
  force = FALSE,
  ...
)

Arguments

object	a fitted Naive Bayes textmodel
newdata	dfm on which prediction should be made
type	the type of predicted values to be returned; see Value
force	make newdata's feature set conformant to the model terms
...	not used

Value

predict.textmodel_nb returns either a vector of class predictions for each row of newdata (when type = "class"), or a document-by-class matrix of class probabilities (when type = "probability") or log posterior likelihoods (when type = "logposterior").

See also

textmodel_nb

Examples

# application to LBG (2003) example data
(tmod <- textmodel_nb(data_dfm_lbgexample, y = c("A", "A", "B", "C", "C", NA)))
#> 
#> Call:
#> textmodel_nb.dfm(x = data_dfm_lbgexample, y = c("A", "A", "B", 
#>     "C", "C", NA))
#> 
#> Distribution: multinomial; prior: uniform; smoothing value: 1; 5 training documents; 37 fitted features.
predict(tmod)
#> R1 R2 R3 R4 R5 V1 
#>  A  A  B  C  C  B 
#> Levels: A B C
predict(tmod, type = "logposterior")
#>            A         B         C
#> R1 -2687.853 -6472.926 -7614.264
#> R2 -2687.853 -4013.332 -7147.946
#> R3 -4671.788 -2368.923 -4671.788
#> R4 -7147.946 -4013.332 -2687.853
#> R5 -7614.264 -6472.926 -2687.853
#> V1 -3212.036 -3007.763 -6381.702